Disease-specific mortality may underestimate the total effect of prostate cancer screening

2010 ◽  
Vol 17 (4) ◽  
pp. 204-210 ◽  
Author(s):  
Pim J van Leeuwen ◽  
Ries Kranse ◽  
Timo Hakulinen ◽  
Monique J Roobol ◽  
Harry J de Koning ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Screening ◽  
Prostate Cancer Screening ◽  
Total Effect ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
Disease Specific

969 DISEASE SPECIFIC MORTALITY MAY UNDERESTIMATE THE TOTAL EFFECT OF PROSTATE CANCER SCREENING

2010 ◽  
Vol 9 (2) ◽  
pp. 304
Author(s):  
P.J. Van Leeuwen ◽  
R. Kranse ◽  
T. Hakulinen ◽  
M.J. Roobol ◽  
C.H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Screening ◽  
Prostate Cancer Screening ◽  
Total Effect ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
Disease Specific

scholarly journals Pearls for Prostate Cancer Screening

2017 ◽  
Vol 13 (02) ◽  
pp. 79
Author(s):  
Monique J Roobol ◽  
Keyword(s):  
Prostate Cancer ◽  
Cancer Screening ◽  
Prostate Cancer Screening ◽  
Huge Amount ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
Disease Specific ◽  
Screening Trials

Prostate cancer screening and its effect on disease-specific mortality has been a topic of debate since the early 1990s. In 2009, after publication of the results of two large randomized prostate cancer screening trials showing contradictory results, the debate continued. Meanwhile, based on the huge amount of data from these two trials it became clear there is no one-size-fits-all for prostate cancer screening. Now, with two recent publications, the debate on whether prostate cancer screening affects mortality can finally be ended. The focus is on how to identify those men that can benefit from screening.

Development and validation of a mutivariate model combining cell cycle progression score with CAPRA to predict prostate cancer mortality in a conservatively managed cohort.

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 67-67 ◽  
Author(s):  
Michael K. Brawer ◽  
Matthew R. Cooperberg ◽  
Stephen J. Freedland ◽  
Gregory P. Swanson ◽  
Steven Stone ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle Progression ◽  
Multivariate Model ◽  
Prognostic Information ◽  
Cycle Progression ◽  
Model Combining ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
The Uk ◽  
Disease Specific

67 Background: Prostate cancer outcomes are variable and difficult to predict. Improved tools are needed to appropriately match treatment to a patient’s risk of progression. We developed and validated a multivariate model to predict disease−specific mortality (DSM) by combining clinical parameters (CAPRA score) with a score based on measuring the expression level of cell cycle progression (CCP) genes. Methods: A multivariate prediction model was trained using patients from 4 retrospective cohorts with median clinical follow up of 7.6 years. We used 200 men from the UK diagnosed after TURP, 353 from Scott & White and 388 from UCSF treated with radical prostatectomy, and 118 men from Durham VA treated with EBRT. CCP score was derived from fixed tumor tissue (biopsy or surgical resection). Outcome was either time from treatment to biochemical recurrence (US cohorts) or time from diagnosis to disease specific mortality (UK cohort). The model was validated for predicting time from diagnosis to DSM in 180 men from the UK diagnosed by needle biopsy with clinically localized prostate cancer and managed conservatively (mean/median CAPRA score = 6). Results: A model combining CAPRA with CCP score was fit in the training set by a Cox Proportional Hazards analysis stratified by cohort. The Combined score was defined as 0.39*CAPRA+0.57*CCP score. There were no significant interactions between cohort and CAPRA or CCP score. This suggests that both CCP score and CAPRA confer similar prognostic information regardless of cohort composition, treatment, or specific outcome. In the validation cohort the Combined score was highly prognostic (HR= 2.27, 95%CI: (1.63, 3.16), p = 1.2 x 10−7). By likelihood ratio testing, the Combined score was a better predictor of DSM than CAPRA alone (p = 0.0028). The c−index of the Combined score was 0.75, which was an improvement over CAPRA (c−index 0.71). Conclusions: This multivariate model predicts DSM in a conservatively treated cohort. The model provides prognostic information beyond clinical variables, and can be used to help differentiate aggressive from indolent cancer at diagnosis.

Ability of cell-cycle progression score to predict risk for progression to metastatic disease and disease-specific mortality in prostate cancer patients after prostatectomy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5524-5524
Author(s):  
Gregory P. Swanson ◽  
Steven Stone ◽  
Lauren Lenz ◽  
Todd Cohen
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle ◽  
Metastatic Disease ◽  
Cell Cycle Progression ◽  
Significant Information ◽  
Cycle Progression ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
Disease Specific

5524 Background: Prostate cancer treatment aims to prevent metastatic disease (METS) and disease-specific mortality (DSM). A major challenge is to identify those at highest risk so additional intervention can be initiated earlier when it has a better chance of success. Pathologic parameters alone have limited ability to predict METS and DSM, but data suggests biomarkers can improve risk discrimination. Methods: Eligible patients had: (1) prostate cancer treated with radical prostatectomy (RP; 1988-1995); (2) available tissue for cell-cycle progression (CCP) testing that resulted in a valid score; (3) preoperative prostate-specific antigen (PSA); (4) no neoadjuvant therapy; and (5) clinical follow-up (N = 360). Cancer of the prostate risk assessment post-surgical (CAPRA-S) was combined with CCP into a combined cell-cycle risk score (CCR = 0.38 × CAPRA-S + 0.57 × CCP). Results: Median follow-up was 23.5 years for patients alive at last follow-up. Overall, 11% (41/360) developed METS and 9% (33/360) had DSM. CCP score added significant information to CAPRA-S when predicting METS (p = 0.001) and DSM (p = 0.001). CCR score was also a significant predictor of METS and DSM (p-values < 1×10−8). CCP and CCR scores were prognostic of METS in patients with rising post-RP PSA. Of patients with biochemical recurrence (BCR), 25% (41/163) developed METS. CAPRA-S alone was predictive of these events (p = 0.01) but was significantly improved with the addition of CCP (Hazard Ratio [HR] = 1.69 [95% Confidence Interval (CI) 1.13, 2.52], p = 0.014). CCR was also highly prognostic (HR = 1.56 [95% CI 1.20, 2.03], p = 0.001). CCR score discriminated risk of METS both post-RP and after post-RP BCR (Table). Conclusions: Overall, the CCR score significantly predicted METS and DSM in prostate cancer post-RP and was also highly prognostic in those with a post-RP rising PSA. It is therefore a useful tool for determining who is at greatest risk of treatment failure and may benefit from earlier intervention. [Table: see text]

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